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Nano-Banana MCP Server

Nano-Banana MCP Server

by fjacquet
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

generate_image

Creates a new image from a text prompt for text-to-image generation; use only for new images, not for editing or modifying existing ones.

Instructions

Generate a NEW image from text prompt. Use this ONLY when creating a completely new image, not when modifying an existing one.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelNoModel to use. "gemini-nano-banana-2.1" (default, Nano Banana 2.1 — pro quality at Flash speed), "gemini-3.1-flash-image" (Nano Banana 2, previous default), "gemini-3.1-flash-lite-image" (Nano Banana 2 Lite — fastest and cheapest, 1K output only), "gemini-2.5-flash-image" (legacy fast), or "gemini-3-pro-image" (Nano Banana Pro, highest quality)
presetNoShape by use: "web" (16:9 landscape), "phone" (9:16 portrait), "webconf" (16:9 video-call background for Zoom/Teams), "banner" (4:1 wide strip, e.g. LinkedIn banner), "square" (1:1). Overridden by aspectRatio when both are given. Size is set with imageSize.
promptYesText prompt describing the NEW image to create from scratch
imageSizeNoResolution of the output image. Default: "1K". Use "2K" or "4K" for high-resolution output. "512px" works only on "gemini-3.1-flash-image".
aspectRatioNoAspect ratio of the output image. Omit to let the model choose: new images usually come out landscape (about 16:9) and edits keep the source image's shape. Pass "1:1" for a square image.
thinkingLevelNoHow much the model reasons before drawing. "minimal" is fastest and cheapest; "high" is best for complex prompts (infographics, text in images, several consistent characters). Default: the model's own ("medium" on "gemini-nano-banana-2.1"). "gemini-3.1-flash-image" and "gemini-3.1-flash-lite-image" accept only "minimal" or "high"; "gemini-2.5-flash-image" does not support thinking levels.
useGoogleSearchNoEnable Google Search grounding for fact-aware image generation. Not supported on "gemini-3.1-flash-lite-image".

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.3.0

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden, yet it discloses nothing about cost, latency, whether the image is stored or returned inline, or any permissions/auth needs. The model and thinking-level cost tradeoffs live entirely in the schema, not the prose, leaving the agent unprepared for what a generation call actually produces.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, zero filler, with the creation constraint front-loaded before the exclusion. Nothing could be cut without losing meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a seven-parameter generation tool with no annotations and no output schema, the definition covers the selection decision but not the outcome, such as what the call returns or how failures surface. On the other hand, the schema is rich and largely self-documenting, so the gap is narrow rather than crippling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and each of the seven parameters carries a detailed enum explanation, so the schema does the semantic work. The description adds only the word "NEW" to reinforce that the prompt describes a fresh image, which is a marginal contribution.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ("Generate a NEW image from text prompt") and immediately bounds the scope to creation-from-scratch. It does not name the sibling tool it excludes, but the new-vs-modify boundary makes the split from edit_image inferable without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Use this ONLY when creating a completely new image, not when modifying an existing one" is an explicit when/when-not rule, which is more than most definitions offer. It stops short of naming the alternative tool or covering cases like variations or continuing an edit, so it is clear context rather than full routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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